Development of a Coaching System for Functional Electrical Stimulation Rowing: A Feasibility Study in Able-Bodied Individuals
Bibliographic record
Abstract
Background: Functional electrical stimulation (FES) during rowing has substantial effects on cardiovascular health in individuals with spinal cord injuries. Currently, manual stimulation control where stimulation is operated by rowers is mostly utilized. However, it takes time to obtain the skill to initiate FES at the optimal timing. The purpose of this study was to develop a coaching system that helps rowers to initiate FES at the optimal timing. Methods: The optimal range for FES application was identified based on the electromyography of the left quadriceps in 10 able-bodied individuals (AB). Then, the effects of the coaching system on the timing of button-pressing, power, and work were investigated in 7 AB. Results: Vastus lateralis (VL) activation began consistently before the seat reached the anterior-most position. Therefore, seat position at the onset of VL was used as the variable to control the switch timing in the coaching system. The results revealed significantly higher power and work outputs in the coaching than the no-coaching condition (median power coaching: 19.10 W, power no-coaching: 16.48 W, p = 0.031; median work coaching: 109.74 J, work no-coaching: 65.25 J, p = 0.047). Conclusions: The coaching system can provide the optimal timing for FES, resulting in improved performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".